Tools & Resources

Generalizable AI model predicting cancer immunotherapy response across cancer types and treatments. Integrates multi-modal clinical and genomic features validated across multi-center cohorts. Published in collaboration with Harvard Medical School and Roche pRED.

Transforms molecular descriptors and fingerprints into structured 2D feature maps, enabling CNNs to learn rich spatial patterns for drug property prediction. Integrated into ZairaChem's automated drug discovery pipeline. Published in Nature Machine Intelligence (2021).

Unsupervised multi-omics feature structurization and deep learning toolbox. Organizes high-dimensional omics features into interpretable 2D maps via manifold learning, enabling explainable CNN-based disease diagnosis and biomarker discovery. Published in Nucleic Acids Research (2022). Featured in Cell Press Patterns.

A knowledge-guided graph pooling framework that bridges molecular fingerprints and GNNs. Uses chemical fingerprints to decompose molecular graphs into biochemically meaningful substructures, achieving state-of-the-art performance on 40 benchmarks with built-in interpretability. New preprint on ChemRxiv (2025).

Automated drug design platform enabling a closed loop of design–synthesis–testing–analysis for small molecule lead compounds. Developed in collaboration with NUS synthetic chemistry lab. Accelerates hit-to-lead timelines from months to weeks.

An interdisciplinary AI tool transforming molecular structures into music — making chemistry audible. Converts SMILES strings, structural formulas, or protein sequences into musical compositions. Supports reverse generation: compose a molecule from a melody.

An interactive feature study and clustering visualization tool for biomedical data exploration. Enables hierarchical feature analysis and interpretation across omics and molecular datasets.